Method, System, and Data Storage Device for Automating Solution Prompts Based Upon Semantic Representation

ABSTRACT

Methods, systems, and computer program products for analyzing one or more perceived or technical problems or proposed solutions, and proposing a result are disclosed. In accordance therewith, a query is received as an input, one or more documents that are most closely semantically related to the query are retrieved, a set of concept terms derived from each of the query and the retrieved semantically related documents is obtained, a list of generic Solution Prompts, each of which generic Solution Prompt thereof includes a placeholder for insertion of a word or phrase from the set of concept terms, is provided, and a morphological analysis is applied to combine the list of generic Solution Prompts with the obtained set of concept terms to create a list of Specific Solution Prompts.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/845,205, filed Apr. 10, 2020, which claims priority to U.S.Provisional Patent Application No. 62/840,041, filed Apr. 29, 2019, andwhich applications are incorporated herein by reference in theirentirety.

TECHNICAL FIELD

The present disclosure is directed to systems and methods, associatedapparatuses, and a data storage device for performing analyses andpresenting results thereof, and more specifically, to computer assistedautomated problem solving and innovation.

BACKGROUND

The Theory of Inventive Problem Solving (TRIZ) described by G. S.Altshuller is known for solving technical problems in order to proposeinventive solutions therefor. (See, e.g., Creativity As an Exact Science(ISBN-13: 978-0677212302), which is incorporated herein by reference inits entirety). The 1985 version of classical TRIZ included ARIZ-85c, aconflict solving algorithm, seventy-six (76) Standard Solutions, andAltshuller's version of Laws of Evolution. For purposes of the instantdisclosure, we adopt Altshuller's definitions of technical problems,namely problems in engineered systems that involve a dilemma or atrade-off between two contradictory elements that require an inventivesolution. The TRIZ approach to problem solving is illustrated in FIG. 1and is generally identified by reference numeral 50. The TRIZ approach50 begins with a technical problem 10, for which an individual must findan inventive solution. According to the approach, the Technical Problem10 is transformed into an abstract Standard Problem 20, which is knownto have one or more generally abstract or generic Standard Solutions 30,as described in the TRIZ corpora. From the generic or abstract StandardSolution 30, an Inventive Solution 40 may be obtained, which attemptsresolve the originally presented Technical Problem 10. The StandardProblem 20 and the abstract Standard Solutions 30 are based onAltshuller's study of previously patented inventions.

Soon after Altshuller's first book, the TRIZ heuristics were reduced to40 Inventive Principles and a Conflicts Matrix to guide the use of theprinciples to solve technical problems (See, e.g., 40 Principles: TRIZKeys to Innovation by Genrich Altshuller (Author), Dana W. Clarke(Author), Uri Fedoseev (Illustrator), Steve Rodman (Translator), LevShulyak (Collaborator), Leonid Lerner (Collaborator) (ISBN-13:978-096407405), which is incorporated herein by reference in itsentirety. As described in “A review of TRIZ, and its benefits andchallenges in practice”, by Imoh M. Ilevbare, David Probert, RobertPhaal Technovation 33 (2013) 30-37, which is incorporated herein byreference in its entirety, the conflict matrix and innovative principlesare easy to understand and were widely adopted, but ultimately werefound to be inadequate owing to the facts that: 1.) the TRIZ methodologyis made difficult by the need to find a best abstract problem torepresent a real technical problem, and 2.) by the need to transform anabstract solution into a concrete solution pertaining to the specifictechnical problem. To this day, these steps have been resistant tostandardization or automation. Instead, the skillful application oftrained and experienced experts is typically required, which is beyondthe ordinary skill of engineers attempting to solve difficult andcomplex problems.

To this day, a number of TRIZ software tools have been created andexamples include: TechOptimizer™ and Goldfire™, commercially availablefrom the Invention Machine Corporation; Guided Innovation Toolkit,commercially available from Ideation International; and TriSolver™ andTRIZ GB™, commercially available from Guided Brainstorming LLC. However,the above software tools fail to alter the basic algorithms or resolvethe difficulties in applying TRIZ. Rather, they merely automate theexisting TRIZ processes and provide examples or illustrations to betterunderstand the underlying TRIZ principles. In other words, existingsystems, machines, and methods still require that a user transform theirTechnical Problem into a conflict defining Standard Problem, and thentransform the Standard Solution into an Inventive Solution.

Referring now to FIG. 2 , which illustrates a process 100 for analyzinga text-based query 150 and finding relevant documents from a documentdatabase using a neural network based Artificial Intelligence asdescribed in U.S. Pat. No. 8,548,951, which is incorporated herein byreference in its entirety. As shown in FIG. 2 , a computer baseddocument processor 120 generates machine-generated representations fordocuments and a representation of a query, and then based on therepresentation of the query, identifies documents stored in theinformation archive that are related to the query. As shown in FIG. 2 ,data 110 can be any type of documents, that include, but are not limitedto: patents, publications, published patent applications, etc. Featuresof a document can include, but are not limited to: textual features andsemantic features. Document features (textual or semantic) can include,but are not limited to: key words, concepts, document styles and otherfeatures that may characterize a document. The document representationscan include, but are not limited to: a feature-based vector and asemantic based vector, and possibly other representations of the contentof a document. The information archive 135 is based on a set ofdocuments d_(i) processed to produce a set of document representationsd_(i)D. The information archive 135 may be searched based on indicesgiven a query, using a neural network Artificial Intelligence.

What is needed then is an Artificial Intelligence based machine that cananalyze a problem and apply TRIZ heuristics so as to automaticallypresent specific and/or concrete solutions that are technologicallyappropriate to the problem, and which utilizes resources that are eitheralready part of an existing engineering system, or which can be easilyintroduced using known engineering methods, without requiring extensivetraining in TRIZ methods.

SUMMARY

The subject matter of the instant disclosure generally relates tomachine implemented methods, systems and associated apparatuses thatanalyze a perceived or technical problem or proposed solution andpropose a result.

In one aspect, a method for analyzing a perceived or technical problemor proposed solution and proposing a result in accordance with theinstant disclosure can be implemented on a machine including one or moreprocessors in communication with one or more non-transitory computerreadable storage media that store specialized computer readableinstructions thereon. The machine is capable of communicating on anelectronic communications network, and when the one or more processorsread the specialized instructions, the method includes: receiving, forexample, via the electronic communications network, a first querydescribing the problem as an input (155); retrieving (180) from adocument archive (135) of the one or more non-transitory computerreadable storage media, one or more documents that are most closelysemantically related to the first query (155); obtaining (220), via aresult set summarizer (190), a set of concept terms that are derivedfrom each of the first query (155) and the retrieved one or moresemantically related documents (180); providing a first list of genericSolution Prompts (260), each of which generic Solution Prompt thereofincludes a placeholder for insertion of a relevant word or phrase fromthe set of concept terms (220); and, applying a morphological analysisprocess to combine (270) the first list of generic Solution Prompts(260) with the obtained set of concept terms (220) so as to create asecond list of Specific Solution Prompts (280).

In some aspects, the method includes analyzing each respective SpecificSolution Prompt of the second list of Specific Solution Prompts forrelevance to the first query, generating a numerical score for eachrespective Specific Solution Prompt of the second list of SpecificSolution Prompts based on the relevance, and applying the generatednumerical score (350) so as to prioritize each respective SpecificSolution Prompt of the second list of Specific Solution Prompts. (300).In further aspects, the generated numerical score (350) for eachrespective Specific Solution Prompt of the second list of SpecificSolution Prompts is based on one or more of: a first score (340)comprising a number of relevant documents returned from the documentarchive as a result of a second query performed using each of therespective specific Solutions Prompts from the second list of SpecificSolution Prompts, and a second score (330) comprising a number ofdocuments common to a first portfolio and a second portfolio. In someaspects, the first portfolio (290) comprises a predefined number of mostrelevant documents returned from the document archive by the firstquery, and the second portfolio (310) comprises a predefined number ofmost relevant documents returned from the document archive as a resultof the second query. In some aspects, the predefined number of mostrelevant documents in each of the first and second portfolio is userdefinable.

In still yet some aspects, the generated numerical score for eachrespective Specific Solution Prompt in the second list of SolutionPrompts is obtained by algorithmically combining the first score and thesecond score. In some aspects, the first list of generic SolutionPrompts includes one or more Solution Prompts based on TRIZ. In someaspects, the first list of generic solution prompts includes one or moreof: 40 Inventive Principles based on TRIZ, a list of separationtechniques, 76 Standard Solutions, and Altshuller's Laws of Evolution.

In some aspects of the method, each of the respective Specific SolutionPrompts in the second list of Solution Prompts is prioritized fromlargest to smallest according to the generated numerical score.

In some aspects, the method includes associating a probability with eachrespective Specific Solution Prompt in the second list of SolutionPrompts. In some aspects the probability includes a ratio of thegenerated numerical score for each respective Specific Solution Promptin the second list of Solution Prompts and a sum of the generatednumerical score for all of the Specific Solution Prompts in the secondlist of Solution Prompts.

In some aspects, the method includes tracking the previously presentedSolution Prompts and user interactions with each of the Solution Promptsand prioritizing Solution Prompts based on the user behavior.

In some aspects, the method includes analyzing a query describing aperceived or technical problem or even a proposed solution; retrievingfrom a document archive, one or more documents (e.g., patents ornon-patent technical literature) that is most closely semanticallyrelated to the query; analyzing, by a keyword extractor, the query andthe one or more the retrieved documents to obtain a set of concept termsthat are related to the query, which are derived from each of the queryand the related documents; obtaining a list of generally applicable orgeneric Solution Prompts, e.g., one or more abstract inventive proposalsspecific to a perceived or technical problem or proposed solution, thateach include one or more suggestions as to where a word or phrase may beinserted into the abstract inventive proposal, wherein upon insertion ofa word or phrase pertinent to the perceived or technical problem orproposed solution into the generally applicable or generic SolutionPrompt, renders the generally applicable or generic Solution Prompt morepertinent to the perceived or technical problem or proposed solution;creating a list of specific Solution Prompts by combining the list ofgenerally applicable or generic Solution Prompts with the list ofconcept terms, and using morphological analysis processes to constructand propose possible relevant solutions or results.

In some further aspects, the specific Solution Prompts are analyzed forrelevance to the original query describing the perceived or technicalproblem or proposed solution in order to generate a set of numericalscores specific to each suggestion, which can be used to prioritize thelist of Specific Solution Prompts. The numerical score for eachsuggestion used to prioritize the list of specific Solution Prompts isbased on the product of the number of relevant documents returned fromthe list of all documents when the suggestion is used as a new query andthe number of identical documents that are retrieved from the originalquery and the new query based on the suggestion; looking at a mostrelevant subset of each the original and new query.

In some aspects, the method includes analyzing each respective SpecificSolution Prompt of the second list of Specific Solution Prompts forrelevance to the first query, generating a numerical score for eachrespective Specific Solution Prompt of the second list of SpecificSolution Prompts based on the relevance, and applying the generatednumerical score (350) so as to prioritize each respective SpecificSolution Prompt of the second list of Specific Solution Prompts. (300).In further aspects, the generated numerical score (350) for eachrespective Specific Solution Prompt of the second list of SpecificSolution Prompts is based on one or more of: a first score (340)comprising a number of relevant documents returned from the documentarchive as a result of a second query performed using each of therespective specific Solutions Prompts from the second list of SpecificSolution Prompts, and a second score (330) comprising a number ofdocuments common to a first portfolio and a second portfolio. In someaspects, the first portfolio (290) comprises a predefined number of mostrelevant documents returned from the document archive by the firstquery, and the second portfolio (310) comprises a predefined number ofmost relevant documents returned from the document archive as a resultof the second query. In some aspects, the predefined number of mostrelevant documents in each of the first and second portfolio is userdefinable.

The features, phrases and key words represent knowledge derivedinitially from the query, but also represent a body of documents thatare related to the query and, by extension, to the perceived ortechnical problem or proposed solution. These phrases and keywords canbe combined with the principles from TRIZ to provide context specific,and technology specific solution prompts, or Specific Solution Prompts,for solving the perceived or technical problem or proposed solution.

In some aspects, a system for automatically analyzing a perceived ortechnical problem or proposed solution generally includes a machinecapable of communicating on an electronic communications platform, themachine having at least one processor and a non-transitory computerreadable storage medium storing specialized instructions thereon, whichwhen read by the machine, cause the machine to perform the operationsof: receiving a first query describing the perceived or technicalproblem or proposed solution as an input (155); retrieving (180) from adocument archive (135), one or more documents that are most closelysemantically related to the first query (155); obtaining (220) via aresult set summarizer (190), a set of concept terms that are derivedfrom each of the first query (155) and the retrieved one or moresemantically related documents (180); providing a first list of genericSolution Prompts (260), each of which generic Solution Prompt thereofincludes a placeholder for insertion of a relevant word or phrase fromthe set of concept terms (220); and, applying a morphological analysisprocess to combine (270) the first list of generic Solution Prompts(260) with the obtained set of concept terms (220) so as to create asecond list of Specific Solution Prompts (280).

In some aspects, the system analyzes each respective Specific SolutionPrompt of the second list of Specific Solution Prompts for relevance tothe first query, generates a numerical score for each respectiveSpecific Solution Prompt of the second list of Specific Solution Promptsbased on the relevance, and applies the generated numerical score (350)so as to prioritize each respective Specific Solution Prompt of thesecond list of Specific Solution Prompts. (300). In further aspects ofthe system, the generated numerical score (350) for each respectiveSpecific Solution Prompt of the second list of Specific Solution Promptsis based on one or more of: a first score (340) comprising a number ofrelevant documents returned from the document archive as a result of asecond query performed using each of the respective specific SolutionsPrompts from the second list of Specific Solution Prompts, and a secondscore (330) comprising a number of documents common to a first portfolioand a second portfolio. In some aspects, the first portfolio (290)comprises a predefined number of most relevant documents returned fromthe document archive by the first query, and the second portfolio (310)comprises a predefined number of most relevant documents returned fromthe document archive as a result of the second query. In some aspects,the predefined number of most relevant documents in each of the firstand second portfolio is user definable.

In, still yet, some aspects of the system, the generated numerical scorefor each respective Specific Solution Prompt in the second list ofSolution Prompts is obtained by algorithmically combining the firstscore and the second score. In some aspects, the first list of genericSolution Prompts includes one or more Solution Prompts based on TRIZ. Insome aspects, the first list of generic solution prompts includes one ormore of: 40 Inventive Principles based on TRIZ, a list of separationtechniques, 76 Standard Solutions, and Altshuller's Laws of Evolution.

In some aspects of the system, each of the respective Specific SolutionPrompts in the second list of Solution Prompts is prioritized fromlargest to smallest according to the generated numerical score.

In some aspects, the system includes associating a probability with eachrespective Specific Solution Prompt in the second list of SolutionPrompts. In some aspects the probability includes a ratio of thegenerated numerical score for each respective Specific Solution Promptin the second list of Solution Prompts and a sum of the generatednumerical score for all of the Specific Solution Prompts in the secondlist of Solution Prompts.

In some aspects, the system includes mechanisms to track the SolutionPrompt presented to the user and track user interactions with each ofSolution Prompts, which includes but not limited to review, review time,click and choose as a Solution candidate. The system allows the user togo back and forth on presented Solution Prompts and learns thepresentation rate and the probability of success for each type ofInventive Principles, separation techniques, or Standard solutions, andadjusts the prioritization strategy as the user proceeds. For example,certain types presented Solution Prompts based on scores will be gettinglower presenting probability if the user keeps ignoring them even thoughtheir scores could be high. Certain types of Solutions are not presentedafter some time will be given a chance to present to the user. Thistracking and learning mechanism can work across the boundaries ofqueries and users, which ends up building a concept/principlerelationship network, adding that this network when taking into accountprobabilities may be termed a probabilities relationship network. Theprobabilities relationship network not only links concepts to genericSolution Prompts, principles, techniques or standard solutions withprobabilities, but also includes the interactions among theprinciples/techniques/solutions coupled with specific concepts. Forexample, one type of concept/principle couple often leads to anotherconcept/principle couple. This knowledge learned from user interactionscan be applied to other users with similar context.

In some aspects, the instant disclosure is directed to one or morenon-transitory computer readable storage media storing machine readableinstructions thereon, e.g., computer readable instructions, whichinstructions when read by the machine transform the machine into aspecialized machine capable of performing the operations of receiving afirst query describing the perceived or technical problem or proposedsolution as an input (155); retrieving (180) from a document archive(135), one or more documents that are most closely semantically relatedto the first query (155); obtaining (220) via a result set summarizer(190), a set of concept terms that are derived from each of the firstquery (155) and the retrieved one or more semantically related documents(180); providing a first list of generic Solution Prompts (260), each ofwhich generic Solution Prompt thereof includes a placeholder forinsertion of a relevant word or phrase from the set of concept terms(220); and, applying a morphological analysis process to combine (270)the first list of generic Solution Prompts (260) with the obtained setof concept terms (220) so as to create a second list of SpecificSolution Prompts (280).

Other aspects, features and advantages of one or more embodiments willbe readily appreciable from the following detailed description and fromthe accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The methods, systems, associated apparatuses, data storage device and/orand programming described herein are further described in terms ofexemplary embodiments. These exemplary embodiments are described indetail with reference to the drawings. These embodiments arenon-limiting exemplary embodiments, in which like reference numeralsrepresent similar structures throughout the several views of thedrawings, and wherein:

FIG. 1 is a schematic a diagram of the TRIZ methodology for technicalproblem solving using abstract representations of the problem andsolution;

FIG. 2 is a schematic diagram of a generic semantic search engine, withthe ability to compare a query to a set of document data and generate adocument index of the most semantically relevant results;

FIG. 3 is a schematic diagram of a process flow according to embodimentsof a system and a method for generating a Solution Prompt;

FIG. 4 is a schematic diagram of a process flow according to embodimentsof a system and a method for selecting from a list of possible SolutionPrompts;

FIGS. 5A-B are schematic diagrams of a process flow for scoring a set ofSpecific Solution Prompts;

FIG. 6 is an illustration of an exemplary embodiment of a graphical userinterface in accordance with the instant disclosure;

FIGS. 7A-C are illustrations of an exemplary embodiment of a graphicaluser interface in accordance with the instant disclosure, which depictsthe generation and display of an automated Solution Prompt; and

FIG. 8 is a schematic illustration of an exemplary system architectureconfigured to implement specialized systems and methods according to theinstant disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant teachings. However, it should be apparent to those skilledin the art that the present teachings may be practiced without suchdetails. In other instances, well known methods, procedures, components,and/or circuitry have been described at a relatively high-level, withoutdetail, in order to avoid unnecessarily obscuring aspects of the presentteachings.

The present disclosure generally relates to systems, methods,apparatuses, storage media, and other implementations directed toautomatically analyzing a perceived or technical problem or proposedsolution and generating results, e.g., generating solutions to difficulttechnical problems by automatically generating solution prompts, whichare specific to the problem technology, for purposes of assistingindividuals such as engineers, discover innovative solutions.Particularly, a list of Solution Prompts is created and presented inresponse to a description of a technical problem, a description of aproposed technical solution that includes a description of a technologyand an engineering system, or any other query that sufficientlydescribes a technology or engineering system looking for a creativesolution or improvement or other useful change. Optionally, the SolutionPrompts are presented one at a time as innovation prompts meant toassist a user in overcoming psychological inertia (a catch-all term forthe many mental barriers to finding truly creative solutions) and toarrive with their own solutions to the technical problems they may face.

Adverting now to FIG. 2 , which depicts an exemplary schematic diagramof a Semantic Search System 100 for a method, system, apparatuses andprograms for performing an information search and retrieval. As shown inFIG. 2 , a set of documents, referred to collectively as Data 110 isreceived and is processed by a Document Processor 120 to create afeature-based vector for each document of the set. Typically, thedocuments of the document set 110 include patents, non-patent technicalliterature and the like, but can include any text-based documents thatcontain useful and relevant information for solving a perceived ortechnical problem or proposed solution (also collectively referred to asa technical problem). A unified representation is then created based onthe feature-based vector, that integrates semantic and feature basedcharacterizations of the document by the Document RepresentationGenerator 125 to produce a set of feature-based vectors thatcharacterizes the documents. The feature-based vectors are indexed by aDocument Indexer 130 and the results are stored in a DocumentRepresentation Based Information Archive 135, e.g., a non-transitorycomputer readable storage medium.

A query 150 is received and is processed by a Query Processor 160 andQuery Representation Generator 165 to generate a feature-based vectorthat characterizes the query. A unified representation of the query isthen created based on the feature-based vector, that integrates semanticand feature based characterizations of the query by the QueryRepresentation Generator 165.

The Candidate Search Unit 170 compares the query unified representationto the document unified representations stored in the DocumentRepresentation Based Information Archive 135 to identify the documentsindexed in the data Archive 135 that are related to the query. Thedegree of similarity between the documents and the query are used to bythe Document Retriever 180 to produce a ranked list of documents thatare semantically related to the query.

A results set summarizer 190 performs linguistic analysis on the contentof the indexed documents, e.g., breaking sentences into smaller unitssuch as words, phrases, etc. Frequently used words, such as grammaticalwords “the” and “a”, may or may not be removed. The results setsummarizer 190 further produces an ordered set of keywords or ResultConcepts Terms. The keywords are prioritized by semantic relevance orcount of occurrences of each keyword in each of a plurality of documentscontaining the word or any other method that provides the most relevantkeywords first in a list of keywords. The keyword can be stored using anexisting database management system (e.g., DBMS) or any commerciallyavailable storage means.

TABLE 1 A list of tools available from TRIZ for problem solving *40inventive principles—conceptual solutions to technical and physicalcontradictions. *76 Standard solutions—for solving system problemswithout the need of identifying contradictions. Effects database—whichincludes about 2500 concepts extracted from the body of engineering andscientific knowledge and applied to problem solving. Separationprinciples—for understanding and solving physical contradictions andpoints at solutions from the inventive principles relevant to theproblem. Contradiction matrix—a matrix of 39 technical parameters thatare arranged on the vertical and horizontal axis to interact with oneanother. *Patterns of evolution of technical systems—for identifyingdirections of technology development explained earlier. IFR andideality—an arbitrary system that has all its parts performing at thegreatest possible capacity. Fitting—this is the process of taking a stepback from the IFR (which is a conceptual and unachievable ideal) into arealistic ‘strong’ solution within the constraints of the presentreal-life conditions (Altshuller, 1996). Function analysis—forunderstanding the interactions between all the components of the systemand to draw out the problems arising from the interactions. Substancefield (Su-field) analysis—similar to function analysis, helping to mapout the entire system and point exactly to problems without addingunnecessary details Analysis of system resources—this is the systematicsearch and analysis of resources within and outside the system to thebenefit of the problem situation so that solutions identified are asclose as possible to the ideal final result (IFR). Nine windows (alsoknown as inventive system thinking or system operator or multi-screendiagram of thinking)-used to understand the problem or a technicalsystem in terms of the context (or environment) in which it exists andthe details of the parts within the system itself. Helps to understandhow the problem (its context and details) may change over time, which isuseful for locating solutions. Creativity tools—for overcomingpsychological inertia (mental habits which prevent innovation, clarityof thought and thinking outside the box). These tools includesize-time-cost and method of little men (otherwise known as ‘smartlittle people’). ARIZ (the Algorithm for Inventive Problem Solving)—aseries of steps utilizing an array of TRIZ tools (some of which areexplained above) for finding solutions and innovations. It is reportedto be most suitable for difficult and complicated problems.

Adverting now to Table 1, which is a list of tools available from TRIZfor problem solving. As may be appreciated from Table 1, the TRIZ corpuscan be somewhat complicated and difficult to master—even by those withsignificant expertise and experience in applying the TRIZ protocols.Three of the tools (marked with an asterisk) comprise lists of genericrecommendations or heuristics that can be applied to technical problems.These lists represent some of the abstract solutions 30 that are part ofTRIZ. Finding the correct abstract solution to apply to a standardproblem 20 is not too difficult and tools, such as the conflict matrix,are designed to facilitate finding of the abstract solution. However,finding the best representation of the Standard Problem 20 to connect tothe perceived Technical Problem 10, and figuring out how to translatethe Standard Solution 30 into the actual Solution to the TechnicalProblem can be exceedingly challenging and problematic—this is one orthe reasons it takes years of training to become a TRIZ expert.Adverting now to FIG. 3 , which illustrates a solution that alleviatesthe aforementioned problems by reducing the need for significant orspecialized TRIZ training. As shown in FIG. 3 , a neural-network basedsemantic analysis of the problem statement when presented as a Query 150(See FIG. 2 ) provides a mechanism for combining the keywords or conceptphrases or topic descriptions that are most relevant to the technicalproblem, with the 40 Inventive Principles and 76 Standard Solutions andPatterns of Evolution from TRIZ, for purposes of providing concreterecommendations or specific Solution Prompts that lead to specificsolutions without having to perform the TRIZ Conceptual analysis that isso difficult for most individuals and engineers.

As shown in FIG. 3 , a system 200 according to the instant disclosurefor inventive problem solving, etc., combines TRIZ heuristics withsemantic search abilities (See, e.g., U.S. Pat. No. 8,548,951). A query,which in this case is shown as Problem Description 155, describes aninventive situation or environment that can include, perhaps, one ormore proposed solutions that have issues such as technical conflictsthat prevent implementation of a solution, is received and is processedby Query Processor 160 and Query Representation Generator 165 togenerate a feature-based vector that characterizes the ProblemDescription query. A query unified representation of the ProblemDescription is then created based on the feature-based vector, thatintegrates semantic and feature based characterizations of the query bythe Query Representation Generator 165.

The Candidate Search Unit 170 compares the query unified representationto the document unified representations stored in the DocumentRepresentation Based Information Archive 135 to identify the documentsindexed in the data archive 135 that are related to the ProblemDescription. The degree of similarity between the identified documentsand the query are used by the Document Retriever 180 to produce a listof documents that are semantically related to the Problem Description.

A Results Set Summarizer 190 generates a list of Keyword or ConceptTerms or phrases that are derived from the list of documentssemantically related to the Problem Description. In this step in theprocess, a set of Concept Terms will be used to transform the abstractrecommendations of TRIZ into concrete recommendations that lead toSpecific Solution Prompts. The Results Set Summarizer 190 chooseskeywords, concept terms and phrases from the document results set byrelevance or a number of occurrences of each keyword in each of aspecified plurality of documents, or by any other natural languageprocessing technique for analyzing the relationships between one or moredocuments and the terms they contain to produce a set of conceptsrelated to the documents, and stores this information in Keyword/ConceptTerms Storage 220. The Keyword/Concept Terms Storage 220 can beimplemented using an existing database management system (e.g., DBMS) orany commercially available database.

In this exemplary embodiment, the system 200 also has stored in storagemedia, Innovation Heuristics, such as a list of Generic Solution Prompts260 (e.g. TRIZ Inventive Principles, Standard Solutions and Patterns ofEvolution lists, etc.). Each of these come in the form of shortdeclarative statements that are made generic by the use of abstractplace holders (e.g. “component object”) that represent some part of thetechnical system that is to be improved. Several examples of thesestatement are provided in Table 2. Note that some of the statements donot have place holders for component objects, that is optional and doesnot change the rest of the process.

TABLE 2 Divide the {component object} into independent subsystems.Combine {component object}'s performing the same, like, or relatedfunctions. Make the {component object} porous or use supplementaryporous elements (inserts, covering.) Set the {component object} inoscillating motion. Introduce a feedback.

The Combine Generic Solution Prompts with Concept Terms 270 step createsthe Specific Solutions Prompts 280, which are specific, concrete andhighly relevant proposed solutions to the technical problem, i.e., theyare non-abstract. In this step, the Generic Solution Prompts 260 arecombined with a list of Keyword/Concept Terms, using a join function(also known as morphological analysis) where all possible combinationsare generated. For example, when combining 30 Inventive Principles thathave placeholders for component objects with 10 Concept Terms a list of300 Solutions Prompt is generated.

The final steps in the process are described in detail in FIG. 4 , butbriefly, the hundreds of Specific Solutions Prompts 280 are prioritizedby a Specific Solutions Prompt Prioritizer 300 using a scoring system,and the List of Solution Prompts 390 can be presented to a user in anordered manner, one item at a time starting with the most relevantsolution for consideration when brainstorming new inventive solutions,or alternatively, as will be described later, the List of SolutionPrompts 390 can be randomized and presented in an stochastic order.

Adverting now to FIG. 5A, which depicts an exemplary high-levelschematic diagram of the Specific Solutions Prompt Prioritizer 400,which uses elements of the Semantic Search System 100 to score andprioritize the list of Specific Solutions Prompts 390. The processbegins with the Specific Solution Prompts 280 that were generated instep 270 from the Generic Solutions 260 and the Keyword/Concept Terms(See FIG. 3 ). Each of the respective Specific Solution Prompts 280 areutilized as a query and are analyzed by the Semantic Search System 100described earlier (See FIG. 2 ). For each Specific Solution Prompt 280,one or more documents is generated, referred to as the Prompt ResultsPortfolio 310, which one or more documents are most closely relatedsemantically to the respective Specific Solutions Prompts query. In anembodiment, the Specific Solutions Prompts Results Portfolio 310includes the most relevant 2500 documents returned as a result of thesemantic analysis of the Specific Solution Prompts 280. Also, theoriginal Problem Description 155 is analyzed to produce another set ofone or more documents, the Problem Description Results Portfolio 290,which one or more documents are semantically most closely related to theProblem Description query 155. In a preferred embodiment, the ProblemDescription Results Portfolio 290 includes the most relevant 2500documents. A Comparator 320 examines both portfolios, 290 and 310, tosee how many documents are commonly contained in both portfolios. Theresulting value of documents commonly contained in each portfolio can beused as a measure of the relevance of the analyzed respective SpecificSolution Prompts 310 and will be used to prioritize the results.

Another measure used to prioritize the results is the total number ofrelevant results from the Document Representation Based InformationArchive 135 that are relevant to a query based on the respectiveSpecific Solution Prompts 280. There are various ways to define whethera document is relevant to the search query. In a preferred embodiment,as disclosed in U.S. Pat. No. 8,548,951 (R. Solmer and W. Ruan), acosine similarity can be computed between the semantic code of the queryand semantic code of each of the plurality of documents. A KL divergencemay then be calculated between the residual keyword vector of the queryand the residual keyword vector of each of the plurality of documents.The final similarity score used for ranking the matched documents can bea weighted sum of the cosine similarity and KL divergence distancemeasures. If the sign of the final similarity score is positive, thenthe document is considered to be relevant. This value, the Count of allRelevant Documents 340, is a measure of the sensibility of therespective Specific Solution Prompts 310. In cases where a SpecificSolution Prompt 280 is non-sensical, e.g., the combination of a keywordwith an abstract phrase is not a good fit, a low count is found, and thesuggestion is suppressed.

The Count of Common Documents 330 and the Count of All RelevantDocuments 340 are combined in a Solutions Prompt Scorer 350 to create anoverall estimate of the likelihood that a particular Specific SolutionPrompt 280 will be useful and should be presented to the user. Thepresentation of the Specific Solution Prompts 280 can be performed oneat a time as it is important that the user has an opportunity to reflecton each suggestion to determine how the inventive system may be changedto bring about improvements and solve problems. That is, presenting theentire list of Specific Solution Prompts at once can becounter-productive and overwhelming. Accordingly, there are at least twomain manners by which a list of Specific Solution Prompts may bepresented. The first, is to simply display the list from the highestscoring Specific Solution Prompts to the lowest scoring SpecificSolution Prompts. The second option, referred to as the Score WeightedRandomizer 360, is to user a random number generator so as to randomlydisplay one or more Specific Solution Prompts from the list in aweighted manner. That is, a probability of a Specific Solution Promptbeing displayed can be weighted by the Solution Prompt Score such thathigher scoring Specific Solution Prompts are more likely to be displayedto a user, but potentially any of the Specific Solution Prompts from thelist of Specific Solution Prompts may be displayed to a user.Optionally, the weighted random selection for each turn can be from allof the Solution Prompts or only from those that have not been presentedyet, thereby preventing the same suggestion from being presented morethan once.

FIG. 5A illustrates a scoring process 400 used for Specific SolutionsPrompt Prioritization. These steps are included in the dashed line inFIG. 4 . In the scoring process, a problem description results portfolio290 created from the original problem description 250 is compared to aResults portfolio 310 created from each of the respective SpecificSolution Prompts 280. For purposes of illustration, it is assumed thatthere are n Specific Solution Prompts in the list of all SpecificSolution Prompts 280, referred to in FIG. 5A as SSP1, SSP2, SSP3 . . .SSPn. For each SSP, the Semantic Search System 100 creates a respectiveSSP Results portfolio, SSP1 Results Portfolio, SSP2 Results Portfolio,SSP3 Results Portfolio . . . SSPn Results Portfolio, referred tocollective as Results Portfolio 310. A Comparator 320 examines theintersection of both the Problem Description Results portfolio 290 witheach of the SSP Results Portfolios to produce a respective set of scoresreferred to as SSP1 Count of Common Documents, SSP2 Count of CommonDocuments, SSP3 Count of Common Documents . . . SSPn Count of CommonDocuments, referred to collectively as 330. Each value 330 is a measureof the relevance of the respective Specific Solution Prompt being scoredand can be used to prioritize the results.

FIG. 5B illustrates the details of the a second scoring step 400 usedfor Specific Solutions Prompt Prioritization. This second score is basedon the total number of relevant results from the Document RepresentationBased Information Archive 135 that are relevant to a query based on therespective Specific Solution Prompts. Each of the Specific SolutionPrompts in the list of Specific Solution Prompts 280 is searchedindividually as a query by the Semantic Search System 100, whichincludes a Document Retriever 180 that can provide a list of relevantdocuments from the document archive 135 or, in this case, simply a countof how many relevant documents are found. This is done for eachrespective Specific Solution Prompt to produce a set of values 340, theSSP1 Count of Relevant Documents, the SSP2 Count of Relevant Documents,the SSP3 Count of Relevant Documents . . . the SSPn Count of RelevantDocuments Count. Each value 340 is a measure of the sensibility of therespective Specific Solution Prompts, because where combinations of akeyword and an abstract phrase is not a good fit, a low count is likelyto occur, and the suggestion can be suppressed.

Referring now to FIG. 6 , which illustrates a graphical user interface500, as may be displayed on an electronic display device or monitor of asystem in accordance with the instant disclosure, for operating thesystems and methods according to the instant disclosure. An overallgraphical user interface 500 is shown as including a background 405, anda foreground 410, which includes one or more interactive problem-solvingsoftware modules, e.g., “Problem Analysis”, “Solution Analysis”,“Improve Known Solutions”, “Find New Solutions”, etc., of the graphicaluser interface 500. There are several possible modules which can beapplied, but for expediency, the instant disclosure will hereinafteronly discuss the “Improve Known Solutions” module 415, which can belaunched by selecting interactive virtual button 420.

Upon selecting the interactive virtual button 420, the graphical userinterface(s) of FIGS. 7A-7C can be displayed. FIGS. 7A-C depict severalsteps as information is progressively displayed as described hereafter.Initially, when a user is directed to the screen of FIG. 7A as a resultof selecting virtual button 420 of FIG. 6 , the various input fields430, 440, 450, 460, etc. are empty until that time when the user inputsa description of a technical problem/solution into input field 430. Whenthis is accomplished and the user selects search icon 435, the systemaccording to the instant disclosure undertakes process 200 of FIG. 3 soas to executed by the semantic search engine 100 and produce a list ofkeywords. As shown in FIG. 7A, due to the fact that a user has input aquery into field 430 and selected search button 435, a number ofkeywords 440 most semantically relevant to the input query are displayedto the graphical user interface. It should be appreciated that whileFIG. 7A shows that a total of nine (9) most relevant results (e.g.,keywords) have been displayed to the display, the instant disclosure isnot limited to displaying only nine (9) keywords, and the number ofrelevant results displayed may be defined by a user.

Once the semantic search engine has displayed a number of results orkeywords, the system is now ready to present to the display, one or moreSpecific Solution Prompts based on a combination of the TRIZ corpus andthe keywords—this is accomplished by the systems 300/400 and processespreviously described and illustrated in FIGS. 4-5B. That is, when a userselects the interactive virtual input button 455, e.g., “NewSuggestion”, the systems 300/400 retrieve one or more Specific SolutionPrompts and displays it to the graphical user interface in field 450. Asshown in FIG. 7B, because a user has selected the “New Suggestion”button, the first Specific Solution Prompt of “Make Characteristics ofthe pole (or external environment) changeable to be the best at eachstage of operation” 450 a is displayed in field 450. Where a userselects the “New Suggestion” input button 455 once again, a new SpecificSolution Prompt 450 b is displayed to filed 450 as shown in FIG. 7C. Ifthe virtual input button 455 is repeatedly selected, further SpecificSolution Prompts may be displayed to the user. In other words, each verytime the “New Suggestion” input button 455 is selected, a new SpecificSolutions Prompt is presented to the user. The user may read a SpecificSolution Prompt, reflect upon how it may be applied to the currenttechnical system or problem, and then input information into the “NewSolution” field 460, and then select the “Record New Solution” button465, which records the input information into a storage area or memoryand clears filed 460 so that other information (e.g., concepts, ideas,etc.) based on the same Specific Solution Prompt can be recorded.Alternatively, button 455 can be selected pressed to display a newSpecific Solution Prompt. The entire process can continue to iterate foras long as the user desires, until the user selects the “Next” button470, which displays other routines, including a report generator thatwill create a report of all the recorded solutions, or ends the process.

Note that in an embodiment, the Specific Solution Prompts can be rankedaccording to score and presented in a ranked sequential order as may bedefined by a user, i.e., lowest to highest score/highest to lowestscore, etc. In such case, each Specific Solution Prompt can be presentedonce starting from the beginning of the list to the end of the list. Inthe current implementation, the list can include some 500 SolutionPrompts, so a user may rarely reach the end of the list. At the end, aninformational message can be displayed indicating the same and, and thesoftware can automatically continue from the top of the list.

In other embodiments, a random number generator can be used to selectone Specific Solution Prompt from among all of the possible SpecificSolution Prompts. In such case, Score Weighted Randomizer 360 having aprobability of selecting a given suggestion that is proportional to theratio of the score for each respective Specific Solution Prompt and thesum of the scores of all the Specific Solution Prompts. Optionally, somehigher scoring Specific Solution Prompts can be selected more than once,and other, typically lower scoring Specific Solution Prompts, may not beselected at all. However, due to the creative process and the ability ofindividuals to quickly perceive patterns, this stochastic process ismore effective at stimulating solutions, and users may examine moreSpecific Solution Prompts and generate more new solution ideas thancompared to the embodiment wherein Specific Solution Prompts maybesequentially displayed. Or, optionally, each Specific Solution Promptmay only be presented once. If a previously selected Specific SolutionPrompt is randomly chosen, the system can skip to the next SpecificSolution Prompt until a suggestion that has not yet been displayed in asession is selected.

The system and methods can further include one or more mechanisms totrack the Specific Solution Prompts presented to the user and track userinteractions with each of Specific Solution Prompts, which includes butis not limited to review, time spent reviewing before moving on to thenext Solution Prompt, number of new solution candidates created afterreview before moving on the next Solution Prompt. The system allows theuser to go back and forth on already presented Specific Solution Promptsand learns the presentation rate and the probability of success for eachtype or class of Inventive Principles, heuristic, separation techniques,or Standard solutions underlying the generic Solution Prompt from whichthe respective Specific Solution Prompt was constructed, and adjusts theprioritization strategy as the user proceeds. For example, the scores ofcertain Specific Solution Prompts will be reduced dynamically if theuser keeps ignoring the class of generic Solution Prompt from which therespective Specific Solution Prompt was constructed even though theirinitial scores could be high. Certain types of Solutions that are notpresented initially, after some time will be given a chance to bepresented to the user. This tracking and learning mechanism can workacross the boundaries of queries and users, which ends up building aconcept/principle relationship network database, adding that thisnetwork when taking into account probabilities may be termed aprobabilities relationship network. The probabilities relationship. Theprobabilities relationship network not only links concepts to genericSolution Prompts, principles, heuristics, techniques or standardsolutions with probabilities, but also includes the interactions amongthe principles/techniques/solutions coupled with specific concepts. Forexample, one type of concept/principle couple often leads to anotherconcept/principle couple. This knowledge learned from user interactionscan be applied to other users with similar context.

FIG. 8 depicts an architecture on which the teachings of the instantdisclosure may be implemented and realized and includes a functionalblock diagram illustration of a computer hardware platform whichincludes user interface elements. Computer 1000 may be a general-purposecomputer or a special purpose computer and can be used to implement anycomponent of the present teachings, as described herein. For example,the present teachings may be implemented on a computer such as computer1000, via its hardware, one or more software programs, firmware, orcombinations thereof. Although only one such computer is shown, forconvenience, the computer functions relating to the present teaching asdescribed herein may be implemented in a distributed fashion on a numberof similar computer platforms, to, for example, distribute processingload. Examples of computers and computer systems, environments, and/orconfigurations that may be represented by the components illustrated inFIG. 8 include, but are not limited to, personal computer systems,server computer systems, thin clients, thick clients, laptop computersystems, tablet computer systems, cellular telephones (i.e., smartphones), multiprocessor systems, microprocessor-based systems, networkPCs, minicomputer systems, mainframe computer systems, and distributedcloud computing environments that include any of the above systems ordevices.

The computer 1000, for example, includes one or more communicationsunits 1050 connectable to and from a computer network connected theretoto facilitate data communications. Communications units can includenetwork adapters or interfaces such as a TCP/IP adapter cards, wirelessWi-Fi interface cards, or 3G or 4G wireless interface cards or otherwired or wireless communications links. The network can comprise, forexample, copper wires, optical fibers, wireless transmission, routers,firewalls, switches, gateway computers and/or edge servers. Software anddata used to practice embodiments of the present disclosure can bedownloaded to computer 1000 through communications unit 1050 (i.e., viathe Internet, a local area network, or other wide area network). Fromcommunications unit 1050, software, program software and data can beloaded onto a non-transitory computer readable medium, such as storagedevice 1070.

The computer 1000 also includes a central processing unit (CPU) 1020, inthe form of one or more processors, for executing program instructionsstored on a non-transitory computer readable storage medium. Theexemplary computer platform includes an internal communication bus 1010,program storage and data storage of different forms, e.g., data storagedevice 1070, read only memory (ROM) 1030, or random access memory (RAM)1040, solid state hard drives, semiconductor storage devices, erasableprogrammable read-only memories (EPROM), flash memories, or any othercomputer readable storage media that is capable of storing programinstructions or digital information for various data files to beprocessed and/or communicated by the computer, as well as possiblyprogram instructions to be executed by the CPU. The computer 1000 alsoincludes an I/O component 1060, supporting input/output flows betweenthe computer and other components therein such as user interfaceelements 1080 (e.g., a display device/monitor, and one or more inputdevices such as a keyboard, mouse, touchpad, touchscreen, speaker,microphone, etc.). The computer 1000 may also receive programming anddata via network communications.

Hence, aspects of the methods and processes, as outlined above, may beembodied in programming. Program aspects of the technology may bethought of as “products” or “articles of manufacture” typically in theform of executable code and/or associated data that is carried on orembodied in a type of machine readable medium. Tangible non-transitory“storage” type media include any or all of the memory or other storagefor the computers, processors or the like, or associated modulesthereof, such as various semiconductor memories, tape drives, solidstate drives, disk drives and the like, which may provide storage at anytime for the software programming.

All or portions of the programming and/or software may at times becommunicated through a network such as the Internet or various othercommunications networks. Such communications, for example, may enableloading of the programming and/or software from one computer orprocessor into another, for example, from a management server or hostcomputer of a search engine operator or other systems into the hardwareplatform(s) of a computing environment or other system implementing acomputing environment or similar functionalities in connection withquery/ads matching. Thus, another type of media that may bear thesoftware elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted to tangible“storage” media, terms such as computer or machine “readable medium”refer to any medium that participates in providing instructions to aprocessor for execution.

Hence, a machine-readable medium may take many forms, including but notlimited to, a tangible storage medium, a carrier wave medium or physicaltransmission medium. Non-volatile storage media include, for example,optical or magnetic disks, such as any of the storage devices in anycomputer(s) or the like, which may be used to implement the system orany of its components as shown in the drawings. Volatile storage mediainclude dynamic memory, such as a main memory of such a computerplatform. Tangible transmission media include coaxial cables; copperwire and fiber optics, including the wires that form a bus within acomputer system. Carrier-wave transmission media may take the form ofelectric or electromagnetic signals, or acoustic or light waves such asthose generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of machine or computer-readable mediatherefore include for example: a floppy disk, a flexible disk, harddisk, magnetic tape, any other magnetic medium, a CD-ROM, DVD orDVD-ROM, any other optical medium, punch cards paper tape, any otherphysical storage medium with patterns of holes, a RAM, a PROM and EPROM,a FLASH-EPROM, any other memory chip or cartridge, a carrier wavetransporting data or instructions, cables or links transporting such acarrier wave, or any other medium from which a computer may readprogramming code and/or data. Many of these forms of computer readablemedia may be involved in carrying one or more sequences of one or moreinstructions to a physical processor for execution.

Those skilled in the art will recognize that the present teachings areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it/they may also be implemented as asoftware only solution, e.g., an installation on an existing server. Inaddition, the systems, methods and processes may be implemented asfirmware, firmware/software combination, firmware/hardware combination,or a hardware/firmware/software combination.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on a user's computer,partly on a user's computer, as a stand-alone software package, partlyon a user's computer and partly on a remote computer or entirely on theremote computer or server. In the latter scenario, the remote computermay be connected to a user's computer through any type of network,including a local area network (LAN) or a wide area network (WAN), orthe connection may be made to an external computer (for example, throughthe Internet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present disclosure.

Aspects of the present teachings are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions may also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The following Examples describe results presented upon operation of thesystems and processes according to the instant disclosure, and useroperation of the graphical user interface of FIGS. 6-7B:

Example 1 (See. e.g., FIG. 7A) Current Solution 430:

Design a robot arm actuator with a high torque axial flux permanentmagnet coupled with planetary gear system to deliver a slim topology foreasy loading.Key word list 440 (note that many of the presented keywords do notappear in the original solution query. Instead, the key words arederived from the result documents after the semantic search):Rotor, motor, gear, magnetic, flux, winding, pole, actuator, shaft,rotate, machine, permanent magnet, axial, drive, coil, speed, state,power, rotationSample of the top 20, in sequential order, Specific Solutions Promptsconstructed by system 200 using the first 10 keywords generated and 50TRIZ inventive principles, similar to those shown in Table 2:Charge the pole with a force that is opposite to the direction of theworking force.Support the gear by its interaction with the environment causing upwardforce.Charge the actuator with a force that is opposite to the direction ofthe working force.Charge the rotation with a force that is opposite to the direction ofthe working force.Charge the rotate with a force that is opposite to the direction of theworking force.Make characteristics of the pole (or external environment) changeable tobe the best at each stage of operation.Charge the gear with a force that is opposite to the direction of theworking force.Make characteristics of the gear (or external environment) changeable tobe the best at each stage of operation.Prepare safety measures to compensate for the relatively low reliabilityof magnet in advance.Make characteristics of the actuator (or external environment)changeable to be the best at each stage of operation.Make fixed parts of the magnetic or environment movable and make movingparts of the object or environment immovable.Prepare safety measures to compensate for the relatively low reliabilityof pole in advance.Charge the magnetic with a force that is opposite to the direction ofthe working force.Make characteristics of the rotate (or external environment) changeableto be the best at each stage of operation.Make all parts of the actuator operate al fill power and without abreak.Prepare safety measures to compensate for the relatively low reliabilityof gear in advance.Change the pole's phase.Change the gear's degree of flexibility.Make fixed parts of the rotor or environment movable and make movingparts of the object or environment immovable.Change the gear's phase.

Example 2 Current Solution 430:

How to design an out-patient interactive stroke therapy system thatempowers and supports a patient's recovery at home in order toimmediately start improving brain function and improve the strokepatient's fine-motor skills.Key word list 440 (note that many of the keywords do not appear in theoriginal solution query. Instead, the keywords are derived rom theresult documents after the semantic search:Rehabilitation, stroke, training, therapy, treatment, activity,clinical, report, brain, analysis, research, motor, significant,intervention, effects, evaluate, participant, trial, model, impair,outcome, limbSample of the top 20, in sequential order, Specific Solutions Promptsconstructed by system 200 using the first 10 keywords generated and 50TRIZ inventive principles, similar to those shown in Table 2:Support the brain by its interaction with the environment causing upwardforce.Charge the brain with a force that is opposite to the direction of theworking force.Prepare safety measures to compensate for the relatively low reliabilityof brain in advance.Support the rehabilitation by its interaction with the environmentcausing upward force.Prepare safety measures to compensate for the relatively low reliabilityof rehabilitation in advance.Change the brain's degree of flexibility.Prepare safety measures to compensate for the relatively low reliabilityof therapy in advance.Make characteristics of the rehabilitation (or external environment)changeable to be the best at each stage of operation.Place the rehabilitations in the best operating positions in advance andavoid wasting time for their delivery.Make characteristics of the brain (or external environment) changeableto be the best at each stage of operation.Prepare safety measures to compensate for the relatively low reliabilityof activity in advance.Charge the rehabilitation with a force that is opposite to the directionof the working force.Prepare safety measures to compensate for the relatively low reliabilityof clinical in advance.Prepare safety measures to compensate for the relatively low reliabilityof stroke in advance.Charge the activity with a force that is opposite to the direction ofthe working force.Change the rehabilitation's phase.Replace an expensive long life rehabilitation by a set of cheap, shortlife or disposable rehabilitations.Prepare safety measures to compensate for the relatively low reliabilityof training in advance.Change the rehabilitation's temperature.Replace an expensive long life brain by a set of cheap, short life ordisposable brains.

Example 3 Current Solution 430:

How to design a suspension bridge supporting cable system that does noterode nor oxidize and at the same time does not exhibit galvanicreaction between the cable and struts.Key word list 440 (note that many of the keywords presented do notappear in the original solution query. Instead, the keywords are derivedfrom the result documents after the semantic search:Coating, metal, polymer, material, layer, composition, about, stent,water, oxide, solution, temperature, agent, particle, reaction,substrate, alloy, corrosion, weight, acid, surface, implant, mixture,compound, electrodeSample of the top 20, in sequential order, Specific Solutions Promptsconstructed by system 200 using the first 10 keywords generated and 50TRIZ inventive principles, similar to those shown in Table 2:Support the corrosion by its interaction with the environment causingupward force.Charge the corrosion with a force that is opposite to the direction ofthe working force.Prepare safety measures to compensate for the relatively low reliabilityof corrosion in advance.Change the corrosion's temperature.Use phenomena associated with phase changes of a substance. For example,change of its density and volume, heats of transformation, temperatureof a substance during phase transition.Prepare safety measures to compensate for the relatively low reliabilityof metal in advance.Prepare safety measures to compensate for the relatively low reliabilityof stent in advance.Change structure of the corrosion or environment from homogeneous tonon-homogeneous.Reject (discharge, dissolve, cut, fire, melt, evaporate, alter) thestent's part as soon as it has accomplished its function.Charge the composition with a force that is opposite to the direction ofthe working force.Make characteristics of the corrosion (or external environment)changeable to be the best at each stage of operation.Establish the best operating conditions for each part of the metal.Reject (discharge, dissolve, cut, fire, melt, evaporate, alter) thecorrosion's part as soon as it has accomplished its function.Change structure of the metal or environment from homogeneous tonon-homogeneous.Reject (discharge, dissolve, cut, fire, melt, evaporate, alter) thecoating's part as soon as it has accomplished its function.Reject (discharge, dissolve, cut, fire, melt, evaporate, alter) thelayer's part as soon as it has accomplished its fiction.Reject (discharge, dissolve, cut, fire, melt, evaporate, alter) themetal's part as soon as it has accomplished its function.Place the corrosions in the best operating positions in advance andavoid wasting time for their delivery.Reject (discharge, dissolve, cut, fire, melt, evaporate, alter) thepolymer's part as soon as it has accomplished its function.Reject (discharge, dissolve, cut, fire, melt, evaporate, alter) thecomposition's part as soon as it has accomplished its function.

While the foregoing has described what are considered to constitute thepresent teachings and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that theteachings may be applied in numerous applications, only some of whichhave been described herein. It is intended by the following claims toclaim any and all applications, modifications and variations that fallwithin the true scope of the present teachings.

What is claimed is:
 1. A system implementable on a machine comprising atleast one processor adapted to communicate with at least onenon-transitory computer readable storage media, the machine capable ofcommunicating via an electronic communications network and adapted toanalyze at least one or more of a perceived problem, a technicalproblem, a proposed solution, and a proposed result; the system furtheradapted to track a plurality of solution prompts presented to the userand adapted to track user interactions with each of the solutionprompts, the interactions from at least one or more from a group ofreview, review time, user clicks, and choice of solution candidates; andthe tracking adapted to build a probabilities relationship networkbetween at least one or more of inventive principles, heuristics,separation techniques, and standard solutions at least one or more ofsequentially and parallelly.
 2. The system of claim 1, wherein aneural-network based semantic analysis of the problem statement isadapted to be presented as a query adapted to provide a mechanism forcombining at least one or more of keywords, concepts, and topicdescriptions deemed relevant to the technical problem, the systemfurther adapted to provide recommendations for solution prompts leadingto specific solutions with the inventive principles, heuristics,separation techniques, and standard solutions.
 3. The system of claim 2,wherein user interaction calculations are adapted to include at leastone or more of user review, user review time, user clicks, and choice ofsolution candidates, and wherein a machine learning system determinesthe presentation rate and the probability of success for solution prompttypes.
 4. The system of claim 3, wherein the solution prompts aremachine generated using at least one or more of inventive principles,heuristics, separation techniques, and standard solutions, wherein themachine generated solution prompts are adapted to adjust how inventiveprinciples, heuristics, separation techniques, and standard solutionsare prioritized as the user proceeds with the solution prompts; thesystem further adapted to use a tracking and learning mechanism whichlinks concepts to at least one or more of other solution prompts,inventive principles, heuristics, separation techniques, and standardsolutions with probabilities and interactions among the inventiveprinciples, heuristics, separation techniques, and standard solutionscoupled with specific related concepts; and the machine generatedsolution prompts adapted to be used by machine learning for machinegenerating subsequent solution prompts.
 5. The system of claim 4,wherein the solution prompts presented to the user are tracked toinclude at least one or more of review, time spent reviewing, userclicks, and the number of new solution candidates created before theuser reviews the next solution prompt.
 6. The system of claim 5, whereinthe system is adapted for the user to review previously presentedsolution prompts wherein subsequent reviews are tracked to include atleast one or more of review, review time, user clicks, choice ofsolution candidates, and number of new solution candidates created fromwhich to machine generate further solution prompts using at least one ormore of inventive principles, heuristics, separation techniques, andstandard solutions underlying the further solution prompts and adjustingfrom which the respective solution prompts were constructed, and adjustsprioritizing inventive principles, heuristics, separation techniques,and standard solutions as the user proceeds.
 7. The system of claim 6,wherein the scores of one or more solution prompts are reduced if theuser reviews for less time the class of solution prompt from which therespective solution prompt was constructed when compared to the time theuser reviews other classes of solution prompts.
 8. The system of claim6, wherein the scores of one or more solution prompts are adapted to begiven a better chance to be presented to the user than initiallyprioritized.
 9. The system of claim 4, wherein multiple users andqueries may be tracked to create a probabilities relationship networkdatabase that links with probabilities of concepts being relevant for atleast one or more of solution prompts, inventive principles, heuristics,separation techniques, and standard solutions.
 10. The system of claim9, wherein the knowledge learned from user interactions is applied toother users wherein one type of one or more concepts can lead to atleast one or more other concepts.
 11. A method implementable on amachine including communicating with at least one processor at least onenon-transitory computer readable storage media, the machine furthercommunicating by way of an electronic communications network andanalyzing at least one or more of a perceived problem, a technicalproblem, a proposed solution, and a proposed a result; tracking aplurality of solution prompts presented to the user and tracking userinteractions with each of the solution prompts, the interactions from atleast one or more from a group of review, review time, user clicks, andchoice of solution candidates; and the tracking further building aprobabilities relationship network between at least one or more ofinventive principles, heuristics, separation techniques, and standardsolutions at least one or more of sequentially and parallelly.
 12. Themethod of claim 11, including analyzing via a neural-network basedsemantic analysis of the problem statement and presenting as a queryproviding a mechanism for combining at least one or more of keywords,concepts, and topic descriptions deemed relevant to the technicalproblem, further providing recommendations for solution prompts leadingto specific solutions with the inventive principles, heuristics,separation techniques, and standard solutions.
 13. The method of claim12, including calculating user interactions including at least one ormore of user review, user review time, user clicks, and choice ofsolution candidates, and determining with a machine learning system thepresentation rate and the probability of success for solution prompttypes.
 14. The system of claim 13, including machine-generating thesolution prompts using at least one or more of inventive principles,heuristics, separation techniques, and standard solutions, wherein themachine generated solution prompts adjust how inventive principles,heuristics, separation techniques, and standard solutions areprioritized as the user proceeds with the solution prompts; tracking andlearning links between concepts to at least one or more of othersolution prompts, inventive principles, heuristics, separationtechniques, and standard solutions with probabilities and interactionsamong the inventive principles, heuristics, separation techniques, andstandard solutions coupled with specific related concepts; and applyingthe machine generated solution prompts to machine learning for machinegenerating subsequent solution prompts.
 15. The method of claim 14,including tracking solution prompts presented to the user to include atleast one or more of review, time spent reviewing, user clicks, and thenumber of new solution candidates created before the user reviews thenext solution prompt.
 16. The method of claim 15, including reviewingwith the system previously presented solution prompts wherein subsequentreviews are tracked including at least one or more of review, reviewtime, user clicks, choice of solution candidates, and further promptingfrom number of new solution candidates created machine-generatingfurther solution prompts using at least one or more of inventiveprinciples, heuristics, separation techniques, and standard solutionsunderlying the further solution prompts and adjusting from which therespective solution prompts were constructed, and adjusting prioritizinginventive principles, heuristics, separation techniques, and standardsolutions as the user proceeds.
 17. The method of claim 16, includingreducing the scores of one or more solution prompts if the user reviewsfor less time the class of solution prompt from which the respectivesolution prompt was constructed when compared to the time the userreviews other classes of solution prompts.
 18. The method of claim 16,including giving the scores of one or more solution prompts a betterchance to be presented to the user than initially prioritized.
 19. Themethod of claim 14, including tracking multiple users and queries tocreate a probabilities relationship network database that links withprobabilities of concepts being relevant for at least one or more ofsolution prompts, inventive principles, heuristics, separationtechniques, and standard solutions.
 20. The method of claim 19,including applying the knowledge learned from user interactions withother users wherein one type of one or more concepts can lead to atleast one or more other concepts.